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22 True/False questions

  1. BackpropagationThe best-known learning algorithm and neural computing where the learning is done by comparing computed output to desired output of training cases

          

  2. Artificial neural networkAKA "artificial neural network"

          

  3. K-nearest neighborA prediction method for classification as well as regression type prediction problems where the prediction is made based on the similarity to K neighbors.

          

  4. Pattern recognitionA technique of matching an external pattern to a pattern stored in a computer's memory (ie the process of classifying data into predetermined categories). Pattern recognition is used in inference engine, image processing, neural computing and speech recognition.

          

  5. DendritesThe part of a biological neuron that provides inputs to the cell

          

  6. Summation functionThe best-known learning algorithm and neural computing where the learning is done by comparing computed output to desired output of training cases

          

  7. Sigmoid (logical activation) functionAn S-shaped transfer function in the range of 0 to 1.

          

  8. NeuronA cell (ie, processing element) of a biological or artificial neural network.

          

  9. NucleusA cell (ie, processing element) of a biological or artificial neural network.

          

  10. Kohonen's self organizing feature mapA type of neural network model for machine learning

          

  11. Neural networkAKA "artificial neural network"

          

  12. Connection weightThe weight associated with each link in a neural network model. Assessed by neural networks learning algorithms.

          

  13. PerceptronAn early neural network structure that uses no hidden layer.

          

  14. Processing elementsA neuron and a neural network.

          

  15. Hidden layerThe middle layer of an artificial neural network that has three or more layers.

          

  16. Transformation transfer functionA mechanism to add all the inputs coming into a particular neuron.

          

  17. ANNArtificial Neural Network

          

  18. Parallel processingAn advanced computer processing technique that allows a computer to perform multiple processes at once, in parallel.

          

  19. Neural computingAKA "artificial neural network"

          

  20. Supervised learningA method of training artificial neural networks in which sample cases are shown to the network as input and the weights are adjusted to minimize the error in the output.

          

  21. Threshold valueA hurdle value for the output of a neuron to trigger the next level of neurons. Is an output value is smaller than a threshold value, it will not be passed to the next level of neurons.

          

  22. AxonAn outgoing connection (ie, terminal) from a biological neuron.